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We present a novel problem of text-based visual question generation orTextVQG in short. Given the recent growing interest of the document imageanalysis community in combining text understanding with conversationalartificial intelligence, e.g., text-based visual question answering, TextVQGbecomes an important task. TextVQG aims to generate a natural language questionfor a given input image and an automatically extracted text also known as OCRtoken from it such that the OCR token is an answer to the generated question.TextVQG is an essential ability for a conversational agent. However, it ischallenging as it requires an in-depth understanding of the scene and theability to semantically bridge the visual content with the text present in theimage. To address TextVQG, we present an OCR consistent visual questiongeneration model that Looks into the visual content, Reads the scene text, andAsks a relevant and meaningful natural language question. We refer to ourproposed model as OLRA. We perform an extensive evaluation of OLRA on twopublic benchmarks and compare them against baselines. Our model OLRAautomatically generates questions similar to the public text-based visualquestion answering datasets that were curated manually. Moreover, wesignificantly outperform baseline approaches on the performance measurespopularly used in text generation literature.

Look, Read and Ask: Learning to Ask Questions by Reading Text in Images